Using the Output of Detect Tools
![[CXC Logo]](../../imgs/cxc-logo.gif)
CIAO 4.13 Science Threads
Overview
Synopsis:
All three CIAO source detect programs celldetect, wavdetect, and vtpdetect have a common output format, the _src file; see the Output of Detect: _src Files section of the Detect Overview thread for more information. This thread shows how the source list can be displayed, edited, and used in CIAO data analysis.
Last Update: 18 Nov 2020 - Updates for CIAO 4.13. ds9 now can import FITS catalog directly. Added new regphytocel script as an option to convert to celestial coordinates.
Contents
- Getting Started
- Displaying the Source List
- Examining the Source List
- Manipulating the Source List
- Using the Source List in Analysis
- Using detect output with ds9's catalog tool
- Parameter files:
- History
-
Images
- Figure 1: Source list overlaid on the event data
- Figure 2: Finding the ID of a source region
- Figure 3: Confirming a removed source
- Figure 4: Plot of the spectrum
- Figure 5: Composite spectrum
- Figure 6: Event file with sources removed
- Figure 7: ds9 Catalog Tool
- Figure 8: ds9 Catalog Overlay
- Figure 9: ds9 catalog with 2MASS sources overlaid
- Figure 10: Results from Cross Match with 2MASS
- Figure 11: Displaying Cross Match with 2MASS
Displaying the Source List
The sources can easily be displayed overlaid on an image:
unix% ds9 s3_evt2.fits &
Load the region file from the ds9 menu "Regions" → "Load Regions..." → s3_src.reg (or s3_src.fits).
Either command will produce Figure 1.
Figure 1: Source list overlaid on the event data
![[Thumbnail image: ]](detections.thumb300.png)
[Version: full-size]
![[Print media version: ]](detections.png)
Figure 1: Source list overlaid on the event data
The event file is displayed at a blocking factor of 2.
Examining the Source List
The contents of the source list can be viewed with dmlist:
unix% dmlist s3_src.fits blocks -------------------------------------------------------------------------------- Dataset: s3_src.fits -------------------------------------------------------------------------------- Block Name Type Dimensions -------------------------------------------------------------------------------- Block 1: PRIMARY Null Block 2: SRCLIST Table 31 cols x 15 rows
There are 15 sources (one per row) in the file. To see information about the individual sources:
unix% dmlist "s3_src.fits[cols RA,DEC,POS]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW RA DEC POS(X,Y) 1 212.8639501188 52.1921273978 ( 3961.7571428571, 4035.0714285714) 2 212.8474548928 52.2255111480 ( 4035.7917808219, 4279.3205479452) 3 212.8360726227 52.2021137798 ( 4086.8032786885, 4108.1147540984) ... 13 212.7873357718 52.2792122394 ( 4305.0, 4672.3170731707) 14 212.7665879663 52.2989206268 ( 4397.7476635514, 4816.5981308411) 15 212.8535943060 52.3232348586 ( 4008.4683544304, 4994.3797468354)
Here the RA, Dec, and sky position of each source is given. Any of the columns in the file can be used in the DM filter.
Manipulating the Source List
It is easy to delete unwanted sources from or rearrange the order of the ASCII output (region file) of the detect tools via a text editor. Using the Data Model tools, it is just as straightforward to manipulate the FITS source file as well.
Edit the List of Sources
Once the source list is overlaid on the image, you may see some sources that you want to exclude from the analysis. Currently, ds9 does not number the sources in the field of view. However, double-clicking on a source region brings up the region info box, which lists the region "ID". In Figure 2, the source on the left side of the field of view was chosen - the ID is 12. This number corresponds to the row the region occupies in the FITS file.
NOTE: this technique only works with the first region file loaded into ds9. After that, the imager starts counting up. For example, if you load region_a.fits that has 10 sources, then region_b.fits that has 6, the regions of the second file will be counted from 11 onward. One may, of course, always examine the region file visually to determine which rows to delete.
Figure 2: Finding the ID of a source region
![[Thumbnail image: The source of interest is selected and a source information box displays the coordinates and ID number.]](srcid.thumb300.png)
[Version: full-size]
![[Print media version: The source of interest is selected and a source information box displays the coordinates and ID number.]](srcid.png)
Figure 2: Finding the ID of a source region
The selected source has an ID of 12.
To create a new region file that doesn't have row 12 in it, dmcopy is used:
unix% dmcopy "s3_src.fits[#row=1:11,13:15]" s3_1gone_src.fits unix% dmlist s3_1gone_src.fits blocks -------------------------------------------------------------------------------- Dataset: s3_1gone_src.fits -------------------------------------------------------------------------------- Block Name Type Dimensions -------------------------------------------------------------------------------- Block 1: PRIMARY Null Block 2: SRCLIST Table 31 cols x 14 rows
The dmlist command shows that there are only 14 rows in the file now. This new file may be displayed in ds9 (Figure 3) to ensure that the correct source was removed.
Figure 3: Confirming a removed source
![[Thumbnail image: The source list with one source removed is displayed on the data.]](srcrm.thumb300.png)
[Version: full-size]
![[Print media version: The source list with one source removed is displayed on the data.]](srcrm.png)
Figure 3: Confirming a removed source
The unwanted source (blue cross) is no longer present in the region file (green ellipses).
This filter was fairly simple. It is possible to make more complicated filters with the DM syntax. If you wanted to exclude rows (sources) 8, 12, and 14 from the original file:
unix% dmcopy "s3_src.fits[#row=1:7,9:11,13,15]" s3_3gone_src.fits unix% dmlist s3_3gone_src.fits blocks -------------------------------------------------------------------------------- Dataset: s3_3gone_src.fits -------------------------------------------------------------------------------- Block Name Type Dimensions -------------------------------------------------------------------------------- Block 1: PRIMARY Null Block 2: SRCLIST Table 31 cols x 12 rows unix% dmlist s3_3gone_src.fits"[cols ra,dec,net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW RA DEC NET_COUNTS 1 212.8639501188 52.1921273978 63.250 2 212.8474548928 52.2255111480 320.0 3 212.8360726227 52.2021137798 33.43750 4 212.8353651758 52.2027847341 87.81250 5 212.8349227409 52.2031706722 67.6250 6 212.8067722124 52.2280881209 29.31250 7 212.8783563224 52.2399625097 31.4285717010 8 212.8360692107 52.2365737442 20.4285717010 9 212.8309806527 52.2335386574 125.5714263916 10 212.7481005259 52.1982204514 51.1428565979 11 212.7873357718 52.2792122394 32.0 12 212.8535943060 52.3232348586 54.7449989319
Sort on a Particular Column
The tool dmsort makes it possible to sort on any column in the FITS file. To order the rows by NET_COUNTS:
unix% dmlist "s3_src.fits[cols net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW NET_COUNTS 1 63.250 2 320.0 3 33.43750 4 87.81250 5 67.6250 6 29.31250 7 31.4285717010 8 23.4285717010 9 20.4285717010 10 125.5714263916 11 51.1428565979 12 25.3977279663 13 32.0 14 76.7916641235 15 54.7449989319 unix% dmsort s3_src.fits s3_sorted_src.fits keys=net_counts copyall=yes unix% dmlist "s3_sorted_src.fits[cols net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW NET_COUNTS 1 20.4285717010 2 23.4285717010 3 25.3977279663 4 29.31250 5 31.4285717010 6 32.0 7 33.43750 8 51.1428565979 9 54.7449989319 10 63.250 11 67.6250 12 76.7916641235 13 87.81250 14 125.5714263916 15 320.0
The default behavior of dmsort is to organize the results in ascending order. To sort NET_COUNTS in descending order, add a dash ("-") before the column name:
unix% dmsort s3_src.fits s3_descend_src.fits keys=-net_counts copyall=yes unix% dmlist "s3_descend_src.fits[cols net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW NET_COUNTS 1 320.0 2 125.5714263916 3 87.81250 4 76.7916641235 5 67.6250 6 63.250 7 54.7449989319 8 51.1428565979 9 33.43750 10 32.0 11 31.4285717010 12 29.31250 13 25.3977279663 14 23.4285717010 15 20.4285717010
It is also possible to have several sort priorities (i.e. sort by NET_COUNTS, then NET_COUNTS_ERR); see ahelp dmsort for more information on how to sort files.
Filtering the Source List
If you are interested in only using a certain range of data in your analysis, use the DM filtering syntax to create a new source list with only those items.
To keep the sources that have 40 or more net counts:
unix% dmcopy "s3_src.fits[net_counts=40:]" s3_40cts_src.fits unix% dmlist "s3_40cts_src.fits[cols net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW NET_COUNTS 1 63.250 2 320.0 3 87.81250 4 67.6250 5 125.5714263916 6 51.1428565979 7 76.7916641235 8 54.7449989319
or those with NET_COUNTS between 25 and 80:
unix% dmcopy "s3_src.fits[net_counts=25:80]" s3_25-80cts_src.fits unix% dmlist "s3_25-80cts_src.fits[cols net_counts]" data -------------------------------------------------------------------------------- Data for Table Block SRCLIST -------------------------------------------------------------------------------- ROW NET_COUNTS 1 63.250 2 33.43750 3 67.6250 4 29.31250 5 31.4285717010 6 51.1428565979 7 25.3977279663 8 32.0 9 76.7916641235 10 54.7449989319
See ahelp dmfiltering for other variations on the filtering syntax.
Converting Coordinates to Sexagesimal
Often it is desirable to have the list of sources in
sexagesimal format, i.e. (RA,Dec) = (hms,dms). This can
be done in ds9, with prop_precess,
or using the in CIAO 4.13 regphystocel script.
-
The regphystocel script can be used to convert regions in physical coordinates to celestial coordinates.
% regphystocel s3_src.fits s3_src_ds9.reg clob+ regphystocel infile = s3_src.fits outfile = s3_src_ds9.reg wcsfile = text = tag = clobber = yes verbose = 1 mode = ql % cat s3_src_ds9.reg # Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 fk5 ellipse(14:11:27.3480,52:11:31.6586,1.13986",1.04162",105.875) # ellipse(14:11:20.6574,52:12:7.6096,1.19459",1.01787",46.4835) # ellipse(14:11:20.4876,52:12:10.0250,1.14609",1.00387",150.605) # ellipse(14:11:20.3814,52:12:11.4144,1.16916",1.00168",58.3717) # ellipse(14:11:30.8055,52:14:23.8650,2.29888",1.06689",38.4504) # ellipse(14:11:29.1075,52:13:32.9885,1.62626",1.35886",149.574) # ellipse(14:11:23.3928,52:13:31.9038,1.44264",1.20934",38.5049) # ellipse(14:11:20.6566,52:14:11.6654,1.84725",1.58222",20.7717) # ellipse(14:11:19.4383,52:14:0.6877,1.63179",1.03905",29.4662) # ellipse(14:11:13.6108,52:13:41.1906,1.59571",1.09014",30.1276) # ellipse(14:10:59.5441,52:11:53.5936,2.47202",1.38874",162.832) # ellipse(14:10:57.3916,52:11:30.6992,2.68163",2.53277",86.0687) # ellipse(14:11:14.4804,52:16:11.3231,4.25802",2.73418",170.309) # ellipse(14:11:8.9605,52:16:45.1640,4.08829",3.7775",165.325) # ellipse(14:11:4.2612,52:17:54.7220,7.1976",4.75093",19.4732) # ellipse(14:11:4.0251,52:17:55.3782,7.65908",5.67733",17.9386) # ellipse(14:11:24.7655,52:19:23.6054,8.85711",6.83045",0.264078) #
-
In ds9: load the event file and display the source list on top of it. To save the regions in another format:
- Region → Save Regions... → Save As "s3_src_ds9.reg".
- After choosing "OK" in the region filename dialog, a format dialog is opened. Set the format to "CIAO" and the coordinate system to "WCS" and "Equatorial J2000".
The output (an ASCII region file) still contains the shape definitions, but the coordinates are now given as sexagesimal:
unix% cat s3_src_ds9.reg # Region file format: CIAO version 1.0 ellipse(14:11:27.347,+52:11:31.66,0.0189976',0.0173604',105.875) ellipse(14:11:23.389,+52:13:31.84,0.0189597',0.0151168',31.9394) ellipse(14:11:20.657,+52:12:07.61,0.0199099',0.0169644',46.4835) ellipse(14:11:20.487,+52:12:10.03,0.0191015',0.0167311',150.605) ellipse(14:11:20.381,+52:12:11.42,0.019486',0.0166947',58.3717) ellipse(14:11:13.625,+52:13:41.12,0.0163116',0.0149855',114.407) ellipse(14:11:30.805,+52:14:23.87,0.0383147',0.0177815',38.4504) ellipse(14:11:29.107,+52:13:32.99,0.0271043',0.0226476',149.574) ellipse(14:11:20.656,+52:14:11.67,0.0307874',0.0263703',20.7717) ellipse(14:11:19.435,+52:14:00.74,0.0259394',0.0182104',26.632) ellipse(14:10:59.544,+52:11:53.60,0.0412003',0.0231457',162.832) ellipse(14:11:14.446,+52:16:11.20,0.0585538',0.0422066',0.0725874) ellipse(14:11:08.960,+52:16:45.17,0.0681381',0.0629584',165.325) ellipse(14:11:03.981,+52:17:56.12,0.107363',0.0757421',18.5694) ellipse(14:11:24.862,+52:19:23.65,0.12849',0.10075',9.90983)
The file may be redisplayed in ds9, as it retains the CIAO region format.
-
With prop_precess: prop_precess is a proposal tool that may be used to convert the (RA,Dec) in the FITS source list from decimal to sexagesimal format.
unix% dmlist s3_src.fits"[cols ra,dec]" data,clean > decimal.txt unix% prop_precess f j2000/deg t j2000/hms : decimal.txt : hms.txt Opened input file decimal.txt Opened output file hms.txt unix% cat hms.txt RA,Dec J2000.0 RA,Dec J2000.0 212.863950 52.192127 14 11 27.35 +52 11 31.66 212.847455 52.225511 14 11 23.39 +52 13 31.84 212.836073 52.202114 14 11 20.66 +52 12 07.61 212.835365 52.202785 14 11 20.49 +52 12 10.03 212.834923 52.203171 14 11 20.38 +52 12 11.41 212.806772 52.228088 14 11 13.63 +52 13 41.12 212.878356 52.239963 14 11 30.81 +52 14 23.87 212.871281 52.225830 14 11 29.11 +52 13 32.99 212.836069 52.236574 14 11 20.66 +52 14 11.67 212.830981 52.233539 14 11 19.44 +52 14 00.74 212.748101 52.198220 14 10 59.54 +52 11 53.59 212.810193 52.269778 14 11 14.45 +52 16 11.20 212.787336 52.279212 14 11 08.96 +52 16 45.16 212.766588 52.298921 14 11 03.98 +52 17 56.11 212.853594 52.323235 14 11 24.86 +52 19 23.65
Using the Source List in Analysis
Before beginning any analysis with the source list, be aware that the scaling of the size of each region is controlled by the ellsigma parameter in each of the detect tools. From the help files:
ellsigma is a multiplicative factor applied to sigma ... to scale the major and minor axes of the ellipses for each source. ellsigma affects both the outfile and the ASCII region file (regfile). This feature is included so that the graphics overlay will be more visible and under the user's control.
Carefully consider a reasonable value for ellsigma before using these regions for analysis.
Creating Individual Spectra
It is possible to create a spectrum from any of the regions in the source list without having to divide up the file. To extract a PI spectrum of the second source in the file and bin it by a factor of 5:
unix% punlearn dmextract unix% dmextract "s3_evt2.fits[sky=region(s3_src.fits[#row=2])][bin pi=::1]" \ spectrum.fits
The contents of the parameter file may be checked using plist dmextract.
The resultant spectrum can be plotted in python using matplotlib.
$ python from pycrates import read_file import matplotlib.pyplot as plt tab = read_file("spectrum.fits") xcol = tab.get_column("channel") ycol = tab.get_column("counts") def mklabel(col): lab = col.name if hasattr(col, "unit") and len(col.unit)>0: lab = lab + ' [{}]'.format(col.unit) return(lab) xlab=mklabel(xcol) ylab=mklabel(ycol) plt.plot(xcol.values, ycol.values) plt.xlim(0, 100) plt.xlabel(xlab) plt.ylabel(ylab) plt.title("File: spectrum.fits") plt.savefig("plot1.png")
Figure 4 shows the plot. This data may also be fit in Sherpa, once the proper response files have been created; see the Step-by-Step Guide to Creating ACIS Spectra for Pointlike Sources thread for more information.
Figure 4: Plot of the spectrum
![[Thumbnail image: The spectrum is plotted as solid blue line without symbols or errors.]](plot1.thmb.png)
[Version: full-size]
![[Print media version: The spectrum is plotted as solid blue line without symbols or errors.]](plot1.png)
Figure 4: Plot of the spectrum
This spectrum was created from a single source in the event file (id=2).
Creating Multiple Spectra
To create an individual spectrum for each of the sources, use the igrid(1:n:steps) stack syntax, where n is the number of sources in the file.
For s3_src.fits, which has 15 sources in it:
unix% dmextract "s3_evt2.fits[sky=region(s3_src.fits[component=igrid(1:15:1)])][bin pi=::1]" \ multi_spectra_phaII.fits op=pha2
The output file is a type II PHA file that has one spectrum per row:
unix% dmlist multi_spectra_phaII.fits blocks -------------------------------------------------------------------------------- Dataset: multi_spectra_phaII.fits -------------------------------------------------------------------------------- Block Name Type Dimensions -------------------------------------------------------------------------------- Block 1: PRIMARY Null Block 2: SPECTRUM Table 7 cols x 15 rows Block 3: GTI7 Table 2 cols x 1 rows
For more information on working with this type of file, see the Displaying the Spectrum section of the Examining Grating Spectra and Regions: PHA2 files thread (the information in that section applies to imaging files as well).
Creating a Composite Spectrum
It is also possible to extract a composite spectrum of all the (faint) sources in a field for a single spectral analysis. This may be useful in the case where the sources might be of similar characteristics, but there are not enough counts in any given source to do individual spectra.
Here we have deleted the sources associated with 3C295 (IDs 3-5):
unix% dmcopy "s3_src.fits[#row=1:2,6:]" s3_nocore_src.fits
Now we can use dmextract to accept all the regions together and create a spectrum:
unix% dmextract "s3_evt2.fits[sky=region(s3_nocore_src.fits)][bin pi=::5]" \ spectrum_faint.fits
Again, the resultant spectrum can be plotted :
$ python from pycrates import read_file import matplotlib.pyplot as plt tab = read_file("spectrum_faint.fits") xcol = tab.get_column("channel") ycol = tab.get_column("counts") def mklabel(col): lab = col.name if hasattr(col, "unit") and len(col.unit)>0: lab = lab + ' [{}]'.format(col.unit) return(lab) xlab=mklabel(xcol) ylab=mklabel(ycol) plt.plot(xcol.values, ycol.values) plt.xlim(0,100) plt.xlabel(xlab) plt.ylabel(ylab) plt.title("File: spectrum_faint.fits") plt.savefig("plot2.png")
Figure 5 shows the plot.
Figure 5: Composite spectrum
![[Thumbnail image: The spectrum is plotted as solid blue line without symbols or errors.]](plot2.thmb.png)
[Version: full-size]
![[Print media version: The spectrum is plotted as solid blue line without symbols or errors.]](plot2.png)
Figure 5: Composite spectrum
The composite spectrum was created by combining counts from 14 sources.
As mentioned before, this data may also be fit in Sherpa, once the proper response files have been created; see the Step-by-Step Guide to Creating ACIS Spectra for Pointlike Sources thread for more information.
Removing Unwanted Point Sources
Often it is desirable to remove point sources from an image for both cosmetic and scientific reasons. For example, if you are analyzing an extended source, an unfortunately located point source may affect the count rates in the object.
Suppose that we want to remove all the point sources from our field of view, except for the three in the core (sources 3-5). First, create a source list that excludes these three regions:
unix% dmcopy "s3_src.fits[#row=1:2,6:15]" s3_holes_src.fits
Now filter the event file with those regions:
unix% dmcopy "s3_evt2.fits[exclude sky=region(s3_holes_src.fits)]" s3_holes_evt2.fits unix% ds9 s3_holes_evt2.fits &
The resulting image, Figure 6, has holes where the sources used to be. The Obtain and Fit a Radial Profile thread illustrates another case in which you might use this syntax (see the Removing Contaminating Point Sources section).
Figure 6: Event file with sources removed
![[Thumbnail image: The sources have been removed from the event file.]](holes.thumb300.png)
[Version: full-size]
![[Print media version: The sources have been removed from the event file.]](holes.png)
Figure 6: Event file with sources removed
The source regions are replaced by areas of zero counts.
A more sophisticated means of removing point sources is to use dmfilth. This tool removes the point source and then fills in the hole where the source was. See ahelp dmfilth for more information and the Identify and Remove Point Sources section of the Image of Diffuse Emission thread for an example.
Using detect output with ds9's catalog tool
Often we think of using the output of the source detect tools as spatial filters or regions. These filters may be used to extract spectra or light curves, or even just smaller images for spatial analysis. However, the source lists, properly vetted, is also a catalog of sources with properties such as fluxes.
ds9 has a very power catalog tool that allows users to overlay multiple catalogs on an image and to perform various kinds of analysis including cross-matching between multiple catalogs. Users unfamiliar with the ds9 catalog may find this tutorial useful:
DS9 v8.2 which is included with CIAO 4.13 now supports importing
FITS files directly as catalogs.
To load the detect output as a catalog, we use the Catalog tool.
- Goto Analysis → Catalog Tool ... and a new window will appear.
- In that window goto File → Import → FITS ...
- Select the file, s3_expmap_src.fits from file browser
You will have something resembling Figure 7 and the sources will be identified in the main ds9 window with Figure 8.
Figure 7: ds9 Catalog Tool
![[Thumbnail image: ]](ds9_catalog_tool.thumb300.png)
[Version: full-size]
![[Print media version: ]](ds9_catalog_tool.png)
Figure 7: ds9 Catalog Tool
The ds9 catalog after importing the source list created by running celldetect with an exposure map.
Figure 8: ds9 Catalog Overlay
![[Thumbnail image: ]](ds9_catalog_overlay.thumb300.png)
[Version: full-size]
![[Print media version: ]](ds9_catalog_overlay.png)
Figure 8: ds9 Catalog Overlay
Catalog sources are by default identified with green circle points. This can be changed in the Symbol editor that is accessed via the Catalog Tool window.
Once the catalog is loaded you can then plot various of the sources properties, modify the symbols, or even do a cross match with another catalog.
For example, we can load overlay the 2MASS Point Sources catalog by simply going to Analysis → Catalogs → Infrared → 2MASS Point Sources. This will open a new Catalog Tool window, and after changing the symbol to boxes and making them Magenta, we get an image like Figure 9
Figure 9: ds9 catalog with 2MASS sources overlaid
![[Thumbnail image: ]](ds9_2mass_overlay.thumb300.png)
[Version: full-size]
![[Print media version: ]](ds9_2mass_overlay.png)
Figure 9: ds9 catalog with 2MASS sources overlaid
Both the Chandra source list (green circles) and the 2MASS Point Source catalog (magenta squares) are now overlaid on the image.
Now with two catalogs loaded you can perform a simple cross match using the Analysis → Catalogs → Match tool that finds 3 matches using the default search radius. The result is a new Catalog Tool window (Figure 10) and after editing the shape to be a cyan colored box, we can overlay with the other two catalogs as in Figure 11.
Figure 10: Results from Cross Match with 2MASS
![[Thumbnail image: ]](cattool_cross_match.thumb300.png)
[Version: full-size]
![[Print media version: ]](cattool_cross_match.png)
Figure 10: Results from Cross Match with 2MASS
The results from doing a cross match of our source list with the 2MASS Point Source catalog. Only 3 matches are identified.
Figure 11: Displaying Cross Match with 2MASS
![[Thumbnail image: ]](cross_match_zoom.thumb300.png)
[Version: full-size]
![[Print media version: ]](cross_match_zoom.png)
Figure 11: Displaying Cross Match with 2MASS
The results from doing a cross match of our source list with the 2MASS Point Source catalog (large cyan boxes) overlaid with the original image, Chandra sources (small green boxes), and 2MASS sources (magenta circles)
There are many things that can be done with the data once it has been converted to catalog format and loaded into ds9. The symbol editor can be used to create very complex annotations, properties can be plotted, and with a SAMP hub running, the data can be shared among various other Virtual Observatory applications including topcat.
Parameters for /home/username/cxcds_param/dmextract.par #-------------------------------------------------------------------- # # DMEXTRACT -- extract columns or counts from an event list # #-------------------------------------------------------------------- infile = s3_evt2.fits[sky=region(s3_src.fits[#row=2])][bin pi=::5] Input event file outfile = spectrum_2.fits Enter output file name (bkg = ) Background region file or fixed background (counts/pixel/s) subtraction (error = gaussian) Method for error determination(poisson|gaussian|<variance file>) (bkgerror = gaussian) Method for background error determination(poisson|gaussian|<variance file>) (bkgnorm = 1.0) Background normalization (exp = ) Exposure map image file (bkgexp = ) Background exposure map image file (sys_err = 0) Fixed systematic error value for SYS_ERR keyword (opt = pha1) Output file type: pha1 (defaults = ${ASCDS_CALIB}/cxo.mdb -> /soft/ciao-4.0/data/cxo.mdb) Instrument defaults file (wmap = ) WMAP filter/binning (e.g. det=8 or default) (clobber = no) OK to overwrite existing output file(s)? (verbose = 0) Verbosity level (mode = ql)
History
03 Jan 2005 | reviewed for CIAO 3.2: no changes |
19 Dec 2005 | updated for CIAO 3.3: default value of dmextract error and bkgerror parameters is "gaussian", screen output updated accordingly; updated files in detect data tarfile |
01 Dec 2006 | updated for CIAO 3.4: ChIPS version |
17 Jan 2008 | updated for CIAO 4.0: ChIPS plotting syntax updated; ds9 now automatically looks for the "[SRCLIST]" extension in the region file, so it doesn't have to be specified; deg2sexag.sl has not been updated for CIAO 4.0; removed data tarfile; deg2sexag.sl script replaced by prop_precess |
13 Jan 2009 | updated for CIAO 4.1: provided both Python and S-lang syntax for ChIPS; images are inline |
29 Jan 2010 | updated for CIAO 4.2: changes to the ds9 region file format menu |
13 Jan 2011 | reviewed for CIAO 4.3: no changes |
09 Jan 2012 | reviewed for CIAO 4.4: minor updates to screen output due to changes in the celldetect source list |
03 Dec 2012 | Review for CIAO 4.5; minor version updates; minor prop_precess changes. |
25 Apr 2013 | Added new section to use detect output with ds9 catalog tool. |
25 Nov 2013 | Review for CIAO 4.6. No changes. |
16 Dec 2014 | Reviewed for CIAO 4.7; minor text edits. |
29 Dec 2016 | Reviewed for CIAO 4.9; minor edits only. |
28 Mar 2019 | Replace plots with matplotlib. Updated src2tsv script. |
18 Nov 2020 | Updates for CIAO 4.13. ds9 now can import FITS catalog directly. Added new regphytocel script as an option to convert to celestial coordinates. |